if gradnorm < gradnorm0 * tolgrad or gradnorm < 1e-12:
print '\nGradient sufficiently reduced -- optimization stopped'
break
# search direction
tolcg = min(0.5, np.sqrt(gradnorm / gradnorm0))
cgiter, cgres, cgid, tolcg = compute_searchdirection(
waveobj, 'Newt', tolcg)
myplot.set_varname('srchdir' + str(iter))
myplot.plot_vtk(waveobj.srchdir)
# line search
cost_old = waveobj.cost
statusLS, LScount, alpha = bcktrcklinesearch(waveobj, 12)
cost = waveobj.cost
print '{:11.3f} {:12.2e} {:10d}'.\
format(alpha, tolcg, cgiter)
# stopping criterion (cost)
if np.abs(cost - cost_old) / np.abs(cost_old) < tolcost:
print 'Cost function stagnates -- optimization stopped'
break
# Compute eigenspectrum
waveobj.alpha_reg = 0.0 # no regularization
Omega = np.random.randn(Vm.dim() * 60).reshape((Vm.dim(), 60))
#TODO: need to left-multiply Omega by R^{-1/2}.M (to be checked)
d, U = doublePassG(waveobj, regul.precond, regul.getprecond(), Omega, 40)
np.savetxt(filename + str(Nxy) + '/eigenvalues.txt', d)
# myplot.set_varname('eigenvectors')
# for ii in range(U.shape[1]):
# setfct(waveobj.PDE.a, U[:,ii])
# myplot.plot_vtk(waveobj.PDE.a, ii)
if myrank + 1 < mysize:
b = (myrank + 1) * (Vm.dim() / mysize) + 5
else:
b = Vm.dim()
myrange = range(Vm.dim())[a:b]
print 'Hand-made parallelism: myrank={} (out of {}), myrange=[{}:{}]'.format(\
myrank, mysize, a, b)
Hessfilename = outputdirectory + 'Hessian' + str(freq) \
+ '_' + str(a) + '-' + str(b) + '.dat'
except:
myrange = xrange(Vm.dim())
Hessfilename = outputdirectory + 'Hessian' + str(freq) + '.dat'
# Assemble data Hessian
if mpirank == 0: print 'Assemble data misfit part of the Hessian'
waveobj.alpha_reg = 0.0
Hei, ei = dl.Function(Vm), dl.Function(Vm)
Hessian, VrDM, VcDM = setupPETScmatrix(Vm, Vm, 'dense', mpicomm)
for ii in myrange:
if ii % 100 == 0 and mpirank == 0:
print 'ii={} out of {}'.format(ii, Vm.dim())
ei.vector().zero()
setglobalvalue(ei, ii, 1.0)
waveobj.mult(ei.vector(), Hei.vector())
normvec = Hei.vector().norm('l2')
Hei_arr = Hei.vector().array()
cols = np.where(np.abs(Hei_arr) > 1e-16 * normvec)[0]
for cc, val in zip(cols, Hei_arr[cols]):
global_cc = VcDM.dofs()[cc]
Hessian[ii, global_cc] = val
Hessian.assemblyBegin()
